v1.9.0

1.9.0 - 2024-03-12

Feature

• Add "parallel_trials" parameter in the hyper-parameter tuner to control the number of trials to run in parallel.
• Add support for custom losses.

v1.9.0rc0

1.9.0rc0 - 2024-02-26

Feature

• Add "parallel_trials" parameter in the hyper-parameter tuner to control the number of trials to run in parallel.
• Add support for custom losses.

PYDF 0.1.0

0.1.0 - 2024-01-25

Features

• Added model validation evaluation (for GBTs) and OOB evaluation (for RFs).
• Expose winner-takes-all for Random Forests.
• Added model self evaluation.
• Added ydf.from_tensorflow_decision_forests() for importing TF-DF models.
• Allow feeding datasets as sequence of strings.

Fixes

• Fixes a plotting issue for GBTs without validation loss

Release music

Flötenuhren von 1772 und 1793 - Vivace (Hob XIX:13). Joseph Haydn

v1.8.0

1.8.0 - 2023-11-17

Feature

• Support for GBT distances.
• Remove old snapshots automatically for GBT training.

Fix

• Regression with Mean Squared Error loss and Mean Average error loss
  incorrectly clamped the gradients, leading to incorrect predictions.
• Change dependency from boost to boost_math for faster builds.

Note

The commit associated with this release has a typo in its description.

1.7.0 - 2023-10-20

Feature

• Add support for Mean average error (MAE) loss for GBT.
• Add pairwise distance between examples.
• By default, only keep the last three snapshots when training with a working
  cache to be resilient to training interruptions.

New interface

• Check out the new Python interface in port/python! It's still experimental
  but you can already install it from PyPi with pip install ydf.

v1.6.0

Breaking changes

• The dependency to the distributed gradient boosted trees learner is renamed
  from
  //third_party/yggdrasil_decision_forests/learner/distributed_gradient_boosted_trees
  to
  //third_party/yggdrasil_decision_forests/learner/distributed_gradient_boosted_trees:dgbt.
  Note most case, importing the learners with
  //third_party/yggdrasil_decision_forests/learner:all_learners is
  recommended.
• The training configuration must contain a label. A missing label is no
  longer interpreted as the label being the input feature "".

Feature

• Add support for monotonic constraints for gradient boosted trees.
• Improve speed of dataset reading and writing.

Fix

• Proper error message when using distributed training on more than 2^31
  (i.e., ~2B) examples while compiling YDF with 32-bits example index.
• Fix Window compilation with Visual Studio 2019
• Improved error messages for invalid training configuration
• Replaced outdated dependencies

1.5.0

Feature

• Rename experimental_analyze_model_and_dataset to analyze_model_and_dataset
• Add new GBT loss function POISSON for Poisson log likelihood.
• Go API: Categorical string values available for inspection.
• Improved training speed for unit-weight datasets.
• Support for MHLD oblique decision trees.
• Multi-threaded RMSE computation.
• Added Uint8 inference engine.
• Added Multi-task learning where the output of models trained as "secondary"
  are used as input for the models trained as "primary"

Fix

• Go API: fixed typo on OutOfVocabulary constant.
• Error messages for Uplift models.
• Remove owner leakage in the model compiler.
• Fix buggy restriction for SelGB sampling
• Improve documentation.

1.4.0

Features

• Speed-up the computation of PDP and CEP in the model analysis tool.
• Add compilation of model into .h file.
• [JS port] Add "prefix" argument to model loading method.
• Rename logging function from LOG to YDF_LOG to limit risk of collision with
  TF or Absl.

Fix

• [JS port] Fix memory leak. Release emscripten objects.

1.3.0

1.3.0

Features

• Setting the generic hyper-parameter "subsample" is enough enable random
  subsampling (to need to also set "sampling_method=RANDOM").
• Improve the display of decision tree structures.
• The Hyper-parameter optimizer field "predefined_search_space" automatically
  configures the set of hyper-parameters to explore during automatic
  hyper-parameter tuning.
• Replaces the MEAN_MIN_DEPTH variable importance with INV_MEAN_MIN_DEPTH.

1.2.0

1.2.0 - 2022-11-18

Features

• YDF can load TF-DF models directly (i.e. a TF model with a YDF model in the
  "assets" sub directory).
• Expose confusion tables in a GBT model's analysis.
• Add the "compute_variable_importances" tool to compute variable importances on
  an already trained model.
• Add the "experimental_analyze_model_and_dataset" tool to understand/analyze models.

Yggdrasil Decision Forests 1.1.0

Features

• Early stopping is no longer triggered during first iterations. The initial
  iteration for early stopping can be controlled with the new parameter
  early_stopping_initial_iteration in gradient_boosted_trees.proto.
• Benchmark inference tool does not require for the dataset to contain the
  label column.
• The user can specify the location of the wasm file in the JavaScript port.